Merge pull request #39 from praksharma/development

Migrated to JupyterBook

.gitignore

@@ -2,6 +2,7 @@
 **/__pycache__/
 # Sphinx documentation
 docs/_build/
+docs_legacy/_build/
 
 # Jupyter notebook
 **.ipynb_checkpoints

Tutorials/4. Dataset/1. basic.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to create the training dataset?"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

Tutorials/5. FCNN/1. basic.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of network design"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,

Tutorials/5. FCNN/3. model.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FCNN training"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/Tutorials

@@ -0,0 +1 @@
+../Tutorials

docs/_config.yml

@@ -0,0 +1,33 @@
+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: DeepINN
+author: Prakhar Sharma
+copyright: "2023"
+logo: logo.png
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+  execute_notebooks: force
+
+# Define the name of the latex output file for PDF builds
+latex:
+  latex_documents:
+    targetname: book.tex
+
+# Add a bibtex file so that we can create citations
+bibtex_bibfiles:
+  - references.bib
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/praksharma/DeepINN  # Online location of your book
+  path_to_book: docs  # Optional path to your book, relative to the repository root
+  branch: master  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+  use_issues_button: true
+  use_repository_button: true

docs/_toc.yml

@@ -0,0 +1,17 @@
+# Table of contents
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: intro
+parts:
+  - caption: Installation and contribution
+    chapters:
+    - file: docs_tutorial/installation.md
+    - file: docs_tutorial/contribution.md
+    - file: docs_tutorial/docs_contribution.md
+  # - caption: Contribution
+  #   chapters:
+  #   - file: docs_tutorial/contribution.md
+  # - caption: Documentation compilation
+  #   chapters:
+  #   - file: docs_tutorial/docs_contribution.md

docs/docs_tutorial/contribution.md

@@ -0,0 +1,31 @@
+# Contribution
+Create a `venv` in the root of the repo. Here the assumption is that the `python` is symlink to `python3`.
+```sh
+python -m venv .venv
+```
+Activate the environment.
+```sh
+source .venv/bin/activate
+```
+Confirm that the Python path is updated.
+```sh
+which python
+```
+The `STDOUT` should point to the .venv directory. Now, upgrade the pip.
+```sh
+python -m pip install --upgrade pip
+```
+Install the required packages.
+```sh
+pip install -r requirements.txt
+```
+If you want to build the docs using the same environment, then install the relevant dependencies.
+```sh
+pip install -r docs/requirements.txt
+```
+
+# Testing
+The testing is very simple. Just run the test.py file in the current Python virtual environment.
+```sh
+python test.py
+```

docs/docs_tutorial/docs_contribution.md

@@ -0,0 +1,64 @@
+
+## Documentation compilation
+The doc is created using [Jupyter-book](https://jupyterbook.org/en/stable/intro.html).
+
+### Setting up Jupyter-books
+These steps will allows you to create a basic setup. For more details visit [here](https://jupyterbook.org/en/stable/start/your-first-book.html). 
+1. Create a new Python virtual environment or use an existing one.
+
+```sh
+python3 -m venv jupyter_env
+```
+
+2. Install Jupyter-book.
+```sh
+pip3 install -U jupyter-book
+```
+
+3. Create a template for quick start.
+```sh
+jupyter-book create docs/
+```
+This will create a directory in the ```$PWD``` named ```docs/```. The table of contents are stored in ```_toc.yml``` and the configuration is stored in ```_config.yml```.
+
+4. Building a project
+
+```
+jupyter-book build docs/
+```
+
+For a full rebuild:
+
+```
+jupyter-book build --all docs/
+```
+
+If the toc doesn't update. This will update the entire project.
+
+5. Publish the docs in the new branch.
+
+```
+ghp-import -n -p -f docs/_build/html
+```
+
+6. Deploy website
+Go to "Settings->Pages" of the repo. Set the "Source" to "Deploy from a branch". In the "Branch", select the "gh-pages" branch and location as the "/root".
+
+7. GitHub pages force build
+GitHub pages is known for its laziness. To force deploy the website go to "Setting->Pages". Here, search for the following line:
+
+> Your site was last deployed to the github-pages environment by the pages build and deployment workflow.
+
+Click on "pages build and deployment" and click on the button "Re-run all jobs" on the top right corner.
+
+8. Include notebooks outside the docs/ directory
+You can create a soft link in the book directory to the directory with the notebooks you want to include:
+```sh
+ln -s ../Tutorials ./Tutorials   
+```
+
+You can also link to a document as follows:
+
+```sh
+ln -s ../../README.md ./README.md
+```

docs/docs_tutorial/installation.md

@@ -0,0 +1,53 @@
+# Installation
+## Using pip
+DeepINN can be installed via pip using the following command:
+```sh
+pip install DeepINN
+```
+
+## Docker image
+Pull the image with suitable tagname. The image is available [here](https://hub.docker.com/r/prakhars962/deepinn).
+
+```sh
+docker pull prakhars962/deepinn:tagname
+```
+### CPU Only
+The image opens a jupyter server by default. 
+```sh
+docker run -p 8888:8888 prakhars962/deepinn:pre-release
+```
+
+You can override the jupyter server entrypoint using the following command.
+```sh
+docker run -it --entrypoint /bin/bash prakhars962/deepinn:pre-release
+```
+### GPU passthrough
+First install `nvidia-docker` using this [guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#step-2-install-nvidia-container-toolkit). 
+
+Now run the container with `nvidia-docker`.
+```sh
+nvidia-docker run -it --entrypoint /bin/bash prakhars962/deepinn:pre-release
+```
+This command will bind the `pwd` to `/workspace/tutorials` and open a jupyter-lab with GPU support.
+```sh
+nvidia-docker run -v $(pwd):/workspace/tutorials -p 8888:8888  prakhars962/deepinn:pre-release
+```
+Alternatively, one can run interactive session.
+```sh
+nvidia-docker run -v $(pwd):/workspace/tutorials -it --entrypoint /bin/bash  prakhars962/deepinn:pre-release
+```
+
+### Tagless copy
+Each time you pull the updated image, docker will create a tagless copy of the old one. 
+```sh
+╰─ docker images                              
+REPOSITORY            TAG           IMAGE ID       CREATED             SIZE
+prakhars962/deepinn   pre-release   886808706155   4 minutes ago       6.99GB
+prakhars962/deepinn   <none>        0bb744f6159e   38 minutes ago      6.99GB
+prakhars962/deepinn   <none>        4ffbb67f8447   About an hour ago   6.8GB
+prakhars962/deepinn   <none>        fe16ca34f9d9   About an hour ago   6.8GB
+```
+The only solution is to delete them one by one using the IMAGE_ID.
+```sh
+docker image rm -f IMAGE_ID
+```

docs/intro.md

@@ -0,0 +1,5 @@
+# DeepINN
+[![DeepINN CI](https://github.com/praksharma/DeepINN/actions/workflows/main.yml/badge.svg)](https://github.com/praksharma/DeepINN/actions/workflows/main.yml) [![docker_container](https://github.com/praksharma/DeepINN/actions/workflows/docker.yml/badge.svg)](https://github.com/praksharma/DeepINN/actions/workflows/docker.yml) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/a5c43d9b9e6a45759061ac654bdc1e3f)](https://www.codacy.com/gh/praksharma/DeepINN/dashboard?utm_source=github.com&utm_medium=referral&utm_content=praksharma/DeepINN&utm_campaign=Badge_Grade)![Travis (.org) branch](https://app.travis-ci.com/praksharma/DeepINN.svg?branch=main)
+[![Documentation Status](https://readthedocs.org/projects/deepinn/badge/?version=latest)](https://deepinn.readthedocs.io/en/latest/index.html?badge=latest) [![License](https://img.shields.io/badge/License-AGPL_v3-red.svg)](https://github.com/praksharma/DeepINN/blob/main/LICENSE)
+
+[DeepINN](https://github.com/praksharma/DeepINN) is a deep-learning framework for solving forward  and inverse problem involving PDEs using physics-informed neural networks (PINNs).

docs/references.bib

@@ -0,0 +1,56 @@
+---
+---
+
+@inproceedings{holdgraf_evidence_2014,
+	address = {Brisbane, Australia, Australia},
+	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
+	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
+	publisher = {Frontiers in Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
+	year = {2014}
+}
+
+@article{holdgraf_rapid_2016,
+	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
+	volume = {7},
+	issn = {2041-1723},
+	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
+	doi = {10.1038/ncomms13654},
+	number = {May},
+	journal = {Nature Communications},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
+	year = {2016},
+	pages = {13654},
+	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
+}
+
+@inproceedings{holdgraf_portable_2017,
+	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
+	volume = {Part F1287},
+	isbn = {978-1-4503-5272-7},
+	doi = {10.1145/3093338.3093370},
+	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
+	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
+	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
+	year = {2017},
+	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
+}
+
+@article{holdgraf_encoding_2017,
+	title = {Encoding and decoding models in cognitive electrophysiology},
+	volume = {11},
+	issn = {16625137},
+	doi = {10.3389/fnsys.2017.00061},
+	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
+	journal = {Frontiers in Systems Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
+	year = {2017},
+	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
+}
+
+@book{ruby,
+  title     = {The Ruby Programming Language},
+  author    = {Flanagan, David and Matsumoto, Yukihiro},
+  year      = {2008},
+  publisher = {O'Reilly Media}
+}

docs/requirements.txt

@@ -1,10 +1,3 @@
-sphinx==5.0.2
-sphinx_rtd_theme==1.0.0
-myst-parser==0.18.1
-nbsphinx==0.8.9
-sphinxcontrib-applehelp==1.0.2
-sphinxcontrib-devhelp==1.0.2
-sphinxcontrib-htmlhelp==2.0.0
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.3
-sphinxcontrib-serializinghtml==1.1.5
+jupyter-book
+matplotlib
+numpy

docs_legacy/requirements.txt

@@ -0,0 +1,10 @@
+sphinx==5.0.2
+sphinx_rtd_theme==1.0.0
+myst-parser==0.18.1
+nbsphinx==0.8.9
+sphinxcontrib-applehelp==1.0.2
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==2.0.0
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.5

requirements_docs.txt

@@ -0,0 +1 @@
+jupyter-book

todo.md

@@ -4,19 +4,23 @@ Last work : `DeepINN/constraint/gradients.py`
 ## Geometry
 - [ ] Geometry added but lot of bloats from TorchPhysics. Clean up geometry folder.
 - [ ] Clean up utils folder.
+- [ ] Implement anchor points.
+
 ## Gradients
 - [x] Implement basic gradients.
 - [ ] Implement gradient for multiple output neurons.
-- [ ] Do we need retain_graph=True ?
+- [x] Do we need retain_graph=True?
 
 ## Constraints
 - [X] Implement the prescribed BC part in constraint/ boundary_loss dirichletBC.
 - [X] Implement PDE loss constraint.
 - [ ] Implement gradients.
 - [ ] Implement lazy evaluation of gradients.
+- [ ] Implement more constrainst.
 
 ## Architectures
-- [ ]  Implement fully connected NN.
+- [x] Implement fully connected NN.
+- [ ] Implement more neural networks
 
 ## Tutorials
 - [x] Basic geometry tutorials.